Conditional Variational Autoencoder The variational autoencoder (VAE) defines a generative framework suited for one-to-many generation problem [1]}. While conditional variational autoencoder (CVAE) [2]} is an extension of VAE on the conditional generation problem. As shown in Figure REF  (a), CVAE characterizes the conditional one-to-many generation problem using three random variables: event \(x\) , target \(y\)  and a latent variable \(z\) , which is used for modeling the latent distribution of semantic over targets given an event.
Hence, under a certain inference dimension, with regard to the latent semantic variable \(z\) , the conditional generation problem could be expressed as \(p(y|x)=\int p(y|x,z)p(z|x)dz\) . CVAE models \(p(y|x,z)\)  and \(p(z|x)\)  using deep neural networks (parameterized by \(\theta \) ) \(p_{\theta }(y|x,z)\)  and \(p_{\theta }(z|x)\) . Then as illustrated in Figure REF  (b), \(y\)  could be generated from \(x\)  and \(z\) .
